A time-synchronous Viterbi search is widely used in Hidden Markov Model (HMM) based pattern recognition systems, such as speech recognition and optical character recognition (OCR). Such decoding algorithms are used to efficiently search for a “best” hypothesis from among many different possible hypotheses.
It is, however, infeasible to evaluate all the hypotheses in most cases. Thus, the decoding algorithm employs some pruning algorithms to reduce the number of hypotheses evaluated. Such pruning algorithms usually come with tunable parameters. Those parameters are optimized so that the decoding with the parameters satisfies a condition required by an application in mind. When performing optical character recognition or speech recognition, a computing device may use hidden Markov models to assist in identifying the letters or words.